[FreeCourseSite.com] Udemy - Complete Data Science & Machine Learning A-Z with Python

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[FreeCourseSite.com] Udemy - Complete Data Science & Machine Learning A-Z with Python

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文件列表

  • 42. Competition Section on Kaggle/2. Competitions on Kaggle Lesson 2.mp4 201.0 MB
  • 42. Competition Section on Kaggle/1. Competitions on Kaggle Lesson 1.mp4 197.3 MB
  • 44. Code Section on Kaggle/3. Examining the Code Section in Kaggle Lesson 3.mp4 167.7 MB
  • 43. Dataset Section on Kaggle/1. Datasets on Kaggle.mp4 139.7 MB
  • 41. First Contact with Kaggle/1. What is Kaggle.mp4 136.0 MB
  • 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/6. Recognizing Variables In Dataset.mp4 133.0 MB
  • 41. First Contact with Kaggle/5. Getting to Know the Kaggle Homepage.mp4 128.9 MB
  • 1. Installations/1. Installing Anaconda Distribution for Windows.mp4 124.1 MB
  • 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/1. First Step to the Hearth Attack Prediction Project.mp4 122.8 MB
  • 1. Installations/5. Installing Anaconda Distribution for Linux.mp4 120.3 MB
  • 21. Matplotlib/8. Basic Plots in Matplotlib I.mp4 116.6 MB
  • 46. Other Most Used Options on Kaggle/2. Ranking Among Users on Kaggle.mp4 112.2 MB
  • 27. Linear Regression Algorithm in Machine Learning A-Z/3. Linear Regression Algorithm With Python Part 2.mp4 112.1 MB
  • 44. Code Section on Kaggle/2. Examining the Code Section in Kaggle Lesson 2.mp4 111.0 MB
  • 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/3. Notebook Design to be Used in the Project.mp4 110.0 MB
  • 25. Evaluation Metrics in Machine Learning/2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4 105.1 MB
  • 22. Seaborn/5. Basic Plots in Seaborn.mp4 103.6 MB
  • 25. Evaluation Metrics in Machine Learning/4. Machine Learning With Python.mp4 96.7 MB
  • 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/4. Examining Statistics of Variables.mp4 95.8 MB
  • 14. Functions That Can Be Applied on a DataFrame/3. Aggregation Functions in Pandas DataFrames.mp4 95.1 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 95.1 MB
  • 27. Linear Regression Algorithm in Machine Learning A-Z/5. Linear Regression Algorithm With Python Part 4.mp4 94.4 MB
  • 14. Functions That Can Be Applied on a DataFrame/5. Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes.mp4 92.4 MB
  • 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 88.1 MB
  • 47. Details on Kaggle/1. User Page Review on Kaggle.mp4 85.5 MB
  • 29. Logistic Regression Algorithm in Machine Learning A-Z/3. Logistic Regression Algorithm with Python Part 2.mp4 85.4 MB
  • 23. Geoplotlib/3. Example - 2.mp4 85.1 MB
  • 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 84.3 MB
  • 44. Code Section on Kaggle/1. Examining the Code Section in Kaggle Lesson 1.mp4 83.4 MB
  • 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/5. Examining the Project Topic.mp4 80.2 MB
  • 27. Linear Regression Algorithm in Machine Learning A-Z/2. Linear Regression Algorithm With Python Part 1.mp4 79.9 MB
  • 19. Fundamentals of Python 3/5. Lists, Tuples, Dictionaries and Sets in pyhton.mp4 79.0 MB
  • 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 78.4 MB
  • 47. Details on Kaggle/2. Treasure in The Kaggle.mp4 78.3 MB
  • 29. Logistic Regression Algorithm in Machine Learning A-Z/2. Logistic Regression Algorithm with Python Part 1.mp4 75.7 MB
  • 6. Operations in Numpy Library/2. Arithmetic Operations in Numpy.mp4 75.3 MB
  • 27. Linear Regression Algorithm in Machine Learning A-Z/4. Linear Regression Algorithm With Python Part 3.mp4 73.7 MB
  • 21. Matplotlib/4. Figure, Subplot and Axex.mp4 73.3 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 71.4 MB
  • 11. Structural Operations on Pandas DataFrame/3. Null Values in Pandas Dataframes.mp4 70.2 MB
  • 16. File Operations in Pandas Library/2. Data Entry with Csv and Txt Files.mp4 67.5 MB
  • 49. First Organization/3. Initial analysis on the dataset.mp4 67.1 MB
  • 13. Structural Concatenation Operations in Pandas DataFrame/1. Concatenating Pandas Dataframes Concat Function.mp4 66.9 MB
  • 49. First Organization/1. Required Python Libraries.mp4 66.6 MB
  • 21. Matplotlib/5. Figure Customization.mp4 66.4 MB
  • 20. Object Oriented Programming (OOP)/5. Overriding and Overloading in Object Oriented Programming (OOP).mp4 65.7 MB
  • 13. Structural Concatenation Operations in Pandas DataFrame/4. Merge Pandas Dataframes Merge() Function Lesson 3.mp4 63.1 MB
  • 22. Seaborn/7. Regression Plots and Squarify in Seaborn.mp4 63.0 MB
  • 2. NumPy Library Introduction/2. The Power of NumPy.mp4 62.8 MB
  • 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/3. K Nearest Neighbors Algorithm with Python Part 2.mp4 62.3 MB
  • 19. Fundamentals of Python 3/4. Loops in Python.mp4 61.7 MB
  • 54. Modelling for Machine Learning/4. Hyperparameter Optimization (with GridSearchCV).mp4 61.6 MB
  • 47. Details on Kaggle/4. What Should Be Done to Achieve Success in Kaggle.mp4 61.3 MB
  • 13. Structural Concatenation Operations in Pandas DataFrame/2. Merge Pandas Dataframes Merge() Function Lesson 1.mp4 60.1 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 59.0 MB
  • 13. Structural Concatenation Operations in Pandas DataFrame/6. Joining Pandas Dataframes Join() Function.mp4 58.8 MB
  • 28. Bias Variance Trade-Off in Machine Learning/1. What is Bias Variance Trade-Off.mp4 57.7 MB
  • 22. Seaborn/3. Example in Seaborn.mp4 57.6 MB
  • 21. Matplotlib/9. Basic Plots in Matplotlib II.mp4 57.5 MB
  • 15. Pivot Tables in Pandas Library/2. Pivot Tables in Pandas Library.mp4 56.9 MB
  • 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/5. Examining the Missing Data According to the Analysis Result.mp4 56.4 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/8. Creating a New DataFrame with the Melt() Function.mp4 55.5 MB
  • 54. Modelling for Machine Learning/8. Hyperparameter Optimization (with GridSearchCV).mp4 55.2 MB
  • 46. Other Most Used Options on Kaggle/1. Courses in Kaggle.mp4 54.7 MB
  • 19. Fundamentals of Python 3/10. Exercise - Solution in Python.mp4 54.4 MB
  • 11. Structural Operations on Pandas DataFrame/5. Filling Null Values Fillna() Function.mp4 54.1 MB
  • 23. Geoplotlib/4. Example - 3.mp4 53.8 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 51.8 MB
  • 33. Decision Tree Algorithm in Machine Learning A-Z/3. Decision Tree Algorithm with Python Part 2.mp4 51.3 MB
  • 22. Seaborn/4. Color Palettes in Seaborn.mp4 50.7 MB
  • 8. Series Structures in the Pandas Library/6. Most Applied Methods on Pandas Series.mp4 50.6 MB
  • 32. Hyperparameter Optimization/2. Hyperparameter Optimization with Python.mp4 49.8 MB
  • 29. Logistic Regression Algorithm in Machine Learning A-Z/4. Logistic Regression Algorithm with Python Part 3.mp4 49.6 MB
  • 29. Logistic Regression Algorithm in Machine Learning A-Z/5. Logistic Regression Algorithm with Python Part 4.mp4 49.5 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 49.4 MB
  • 14. Functions That Can Be Applied on a DataFrame/8. Advanced Aggregation Functions Transform() Function.mp4 49.4 MB
  • 19. Fundamentals of Python 3/1. Data Types in Python.mp4 49.4 MB
  • 14. Functions That Can Be Applied on a DataFrame/4. Examining the Data Set 2.mp4 48.8 MB
  • 10. Element Selection Operations in DataFrame Structures/6. Element Selection with Conditional Operations in.mp4 48.6 MB
  • 1. Installations/3. Installing Anaconda Distribution for MacOs.mp4 48.6 MB
  • 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/1. Examining Missing Values.mp4 48.0 MB
  • 5. Indexing, Slicing, and Assigning NumPy Arrays/7. Fancy Indexing of Two-Dimensional Arrrays.mp4 48.0 MB
  • 25. Evaluation Metrics in Machine Learning/3. Evaluating Performance Regression Error Metrics in Python.mp4 47.9 MB
  • 2. NumPy Library Introduction/1. Introduction to NumPy Library.mp4 47.5 MB
  • 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/2. Examining Unique Values.mp4 46.7 MB
  • 53. Preparation for Modelling in Machine Learning/4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 46.0 MB
  • 19. Fundamentals of Python 3/6. Data Type Operators and Methods in Python.mp4 46.0 MB
  • 41. First Contact with Kaggle/3. Registering on Kaggle and Member Login Procedures.mp4 45.6 MB
  • 3. Creating NumPy Array in Python/8. Creating NumPy Array with Random() Function.mp4 45.4 MB
  • 22. Seaborn/6. Multi-Plots in Seaborn.mp4 45.1 MB
  • 14. Functions That Can Be Applied on a DataFrame/2. Examining the Data Set 1.mp4 45.0 MB
  • 53. Preparation for Modelling in Machine Learning/3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 44.9 MB
  • 12. Multi-Indexed DataFrame Structures/1. Multi-Index and Index Hierarchy in Pandas DataFrames.mp4 44.7 MB
  • 33. Decision Tree Algorithm in Machine Learning A-Z/5. Decision Tree Algorithm with Python Part 4.mp4 44.6 MB
  • 22. Seaborn/2. Controlling Figure Aesthetics in Seaborn.mp4 43.8 MB
  • 35. Support Vector Machine Algorithm in Machine Learning A-Z/3. Support Vector Machine Algorithm with Python Part 2.mp4 43.7 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 43.7 MB
  • 54. Modelling for Machine Learning/3. Roc Curve and Area Under Curve (AUC).mp4 43.7 MB
  • 14. Functions That Can Be Applied on a DataFrame/9. Advanced Aggregation Functions Apply() Function.mp4 43.4 MB
  • 19. Fundamentals of Python 3/3. Conditionals in Python.mp4 43.2 MB
  • 46. Other Most Used Options on Kaggle/3. Blog and Documentation Sections.mp4 42.8 MB
  • 13. Structural Concatenation Operations in Pandas DataFrame/5. Merge Pandas Dataframes Merge() Function Lesson 4.mp4 42.7 MB
  • 45. Discussion Section on Kaggle/1. What is Discussion on Kaggle.mp4 42.6 MB
  • 11. Structural Operations on Pandas DataFrame/6. Setting Index in Pandas DataFrames.mp4 41.6 MB
  • 29. Logistic Regression Algorithm in Machine Learning A-Z/6. Logistic Regression Algorithm with Python Part 5.mp4 41.3 MB
  • 8. Series Structures in the Pandas Library/1. Creating a Pandas Series with a List.mp4 41.1 MB
  • 15. Pivot Tables in Pandas Library/1. Examining the Data Set 3.mp4 41.0 MB
  • 23. Geoplotlib/2. Example - 1.mp4 40.7 MB
  • 34. Random Forest Algorithm in Machine Learning A-Z/3. Random Forest Algorithm with Pyhon Part 2.mp4 40.6 MB
  • 34. Random Forest Algorithm in Machine Learning A-Z/2. Random Forest Algorithm with Pyhon Part 1.mp4 40.5 MB
  • 4. Functions in the NumPy Library/4. Concatenating Numpy Arrays Concatenate() Functio.mp4 40.2 MB
  • 10. Element Selection Operations in DataFrame Structures/3. Top Level Element Selection in Pandas DataFramesLesson 1.mp4 40.1 MB
  • 47. Details on Kaggle/3. Publishing Notebooks on Kaggle.mp4 40.1 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 39.9 MB
  • 39. Principal Component Analysis (PCA) in Machine Learning A-Z/1. Principal Component Analysis (PCA) Theory.mp4 39.8 MB
  • 14. Functions That Can Be Applied on a DataFrame/1. Loading a Dataset from the Seaborn Library.mp4 39.6 MB
  • 35. Support Vector Machine Algorithm in Machine Learning A-Z/5. Support Vector Machine Algorithm with Python Part 4.mp4 39.4 MB
  • 39. Principal Component Analysis (PCA) in Machine Learning A-Z/4. Principal Component Analysis (PCA) with Python Part 3.mp4 39.1 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 38.1 MB
  • 53. Preparation for Modelling in Machine Learning/5. Dealing with Outliers – Thalach Variable.mp4 38.0 MB
  • 53. Preparation for Modelling in Machine Learning/6. Dealing with Outliers – Oldpeak Variable.mp4 37.8 MB
  • 20. Object Oriented Programming (OOP)/2. Constructor in Object Oriented Programming (OOP).mp4 37.6 MB
  • 33. Decision Tree Algorithm in Machine Learning A-Z/1. Decision Tree Algorithm Theory.mp4 37.5 MB
  • 4. Functions in the NumPy Library/6. Splitting Two-Dimensional Numpy Arrays Split(),.mp4 37.5 MB
  • 19. Fundamentals of Python 3/2. Operators in Python.mp4 37.4 MB
  • 16. File Operations in Pandas Library/4. Outputting as an CSV Extension.mp4 37.4 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 37.4 MB
  • 35. Support Vector Machine Algorithm in Machine Learning A-Z/2. Support Vector Machine Algorithm with Python Part 1.mp4 37.3 MB
  • 38. Hierarchical Clustering Algorithm in machine learning data science/2. Hierarchical Clustering Algorithm with Python Part 2.mp4 37.2 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 37.2 MB
  • 5. Indexing, Slicing, and Assigning NumPy Arrays/5. Assigning Value to Two-Dimensional Array.mp4 37.1 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/7. Feature Scaling with the Robust Scaler Method.mp4 36.9 MB
  • 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/2. K Nearest Neighbors Algorithm with Python Part 1.mp4 36.7 MB
  • 53. Preparation for Modelling in Machine Learning/2. Visualizing Outliers.mp4 36.6 MB
  • 35. Support Vector Machine Algorithm in Machine Learning A-Z/4. Support Vector Machine Algorithm with Python Part 3.mp4 36.5 MB
  • 30. K-fold Cross-Validation in Machine Learning A-Z/2. K-Fold Cross-Validation with Python.mp4 36.3 MB
  • 16. File Operations in Pandas Library/1. Accessing and Making Files Available.mp4 36.3 MB
  • 20. Object Oriented Programming (OOP)/4. Inheritance in Object Oriented Programming (OOP).mp4 36.3 MB
  • 11. Structural Operations on Pandas DataFrame/4. Dropping Null Values Dropna() Function.mp4 36.2 MB
  • 5. Indexing, Slicing, and Assigning NumPy Arrays/3. Slicing Two-Dimensional Numpy Arrays.mp4 35.9 MB
  • 23. Geoplotlib/1. What is Geoplotlib.mp4 35.8 MB
  • 27. Linear Regression Algorithm in Machine Learning A-Z/1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4 35.7 MB
  • 7. Pandas Library Introduction/1. Introduction to Pandas Library.mp4 35.6 MB
  • 11. Structural Operations on Pandas DataFrame/1. Adding Columns to Pandas Data Frames.mp4 35.2 MB
  • 32. Hyperparameter Optimization/1. Hyperparameter Optimization Theory.mp4 34.7 MB
  • 33. Decision Tree Algorithm in Machine Learning A-Z/6. Decision Tree Algorithm with Python Part 5.mp4 34.3 MB
  • 6. Operations in Numpy Library/3. Statistical Operations in Numpy.mp4 33.6 MB
  • 10. Element Selection Operations in DataFrame Structures/2. Element Selection Operations in Pandas DataFrames Lesson 2.mp4 33.4 MB
  • 26. Supervised Learning with Machine Learning/1. What is Supervised Learning in Machine Learning.mp4 33.2 MB
  • 33. Decision Tree Algorithm in Machine Learning A-Z/2. Decision Tree Algorithm with Python Part 1.mp4 33.1 MB
  • 10. Element Selection Operations in DataFrame Structures/4. Top Level Element Selection in Pandas DataFramesLesson 2.mp4 32.9 MB
  • 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/4. K Nearest Neighbors Algorithm with Python Part 3.mp4 32.9 MB
  • 12. Multi-Indexed DataFrame Structures/3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp4 32.8 MB
  • 13. Structural Concatenation Operations in Pandas DataFrame/3. Merge Pandas Dataframes Merge() Function Lesson 2.mp4 32.0 MB
  • 54. Modelling for Machine Learning/2. Cross Validation.mp4 31.7 MB
  • 37. K Means Clustering Algorithm in Machine Learning A-Z/2. K Means Clustering Algorithm with Python Part 1.mp4 31.4 MB
  • 10. Element Selection Operations in DataFrame Structures/1. Element Selection Operations in Pandas DataFrames Lesson 1.mp4 31.3 MB
  • 8. Series Structures in the Pandas Library/7. Indexing and Slicing Pandas Series.mp4 31.3 MB
  • 54. Modelling for Machine Learning/7. Random Forest Algorithm.mp4 31.2 MB
  • 53. Preparation for Modelling in Machine Learning/11. Separating Data into Test and Training Set.mp4 31.2 MB
  • 37. K Means Clustering Algorithm in Machine Learning A-Z/3. K Means Clustering Algorithm with Python Part 2.mp4 31.1 MB
  • 3. Creating NumPy Array in Python/1. Creating NumPy Array with The Array() Function.mp4 30.9 MB
  • 54. Modelling for Machine Learning/1. Logistic Regression.mp4 30.8 MB
  • 14. Functions That Can Be Applied on a DataFrame/6. Advanced Aggregation Functions Aggregate() Function.mp4 30.6 MB
  • 37. K Means Clustering Algorithm in Machine Learning A-Z/5. K Means Clustering Algorithm with Python Part 4.mp4 30.4 MB
  • 19. Fundamentals of Python 3/8. Functions in Python.mp4 30.3 MB
  • 38. Hierarchical Clustering Algorithm in machine learning data science/3. Hierarchical Clustering Algorithm with Python Part 2.mp4 30.3 MB
  • 55. Conclusion/1. Project Conclusion and Sharing.mp4 30.1 MB
  • 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/1. K Nearest Neighbors Algorithm Theory.mp4 30.0 MB
  • 38. Hierarchical Clustering Algorithm in machine learning data science/1. Hierarchical Clustering Algorithm Theory.mp4 29.9 MB
  • 21. Matplotlib/3. Pyplot – Pylab - Matplotlib.mp4 29.8 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 29.7 MB
  • 21. Matplotlib/2. Using Pyplot.mp4 29.6 MB
  • 29. Logistic Regression Algorithm in Machine Learning A-Z/1. What is Logistic Regression Algorithm in Machine Learning.mp4 29.2 MB
  • 37. K Means Clustering Algorithm in Machine Learning A-Z/4. K Means Clustering Algorithm with Python Part 3.mp4 29.1 MB
  • 24. First Contact with Machine Learning/1. What is Machine Learning.mp4 28.9 MB
  • 21. Matplotlib/6. Plot Customization.mp4 28.7 MB
  • 53. Preparation for Modelling in Machine Learning/1. Dropping Columns with Low Correlation.mp4 28.1 MB
  • 5. Indexing, Slicing, and Assigning NumPy Arrays/1. Indexing Numpy Arrays,.mp4 27.8 MB
  • 4. Functions in the NumPy Library/1. Reshaping a NumPy Array Reshape() Function.mp4 27.4 MB
  • 39. Principal Component Analysis (PCA) in Machine Learning A-Z/2. Principal Component Analysis (PCA) with Python Part 1.mp4 27.3 MB
  • 9. DataFrame Structures in Pandas Library/4. Examining the Properties of Pandas DataFrames.mp4 27.2 MB
  • 54. Modelling for Machine Learning/5. Decision Tree Algorithm.mp4 27.0 MB
  • 53. Preparation for Modelling in Machine Learning/7. Determining Distributions of Numeric Variables.mp4 26.4 MB
  • 20. Object Oriented Programming (OOP)/3. Methods in Object Oriented Programming (OOP).mp4 26.3 MB
  • 12. Multi-Indexed DataFrame Structures/2. Element Selection in Multi-Indexed DataFrames.mp4 25.8 MB
  • 54. Modelling for Machine Learning/6. Support Vector Machine Algorithm.mp4 25.7 MB
  • 14. Functions That Can Be Applied on a DataFrame/7. Advanced Aggregation Functions Filter() Function.mp4 25.6 MB
  • 6. Operations in Numpy Library/4. Solving Second-Degree Equations with NumPy.mp4 25.4 MB
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 25.3 MB
  • 53. Preparation for Modelling in Machine Learning/9. Applying One Hot Encoding Method to Categorical Variables.mp4 25.3 MB
  • 3. Creating NumPy Array in Python/2. Creating NumPy Array with Zeros() Function.mp4 25.2 MB
  • 53. Preparation for Modelling in Machine Learning/8. Transformation Operations on Unsymmetrical Data.mp4 25.2 MB
  • 19. Fundamentals of Python 3/7. Modules in Python.mp4 25.1 MB
  • 21. Matplotlib/7. Grid, Spines, Ticks.mp4 25.0 MB
  • 40. Recommender System Algorithm in Machine Learning A-Z/1. What is the Recommender System Part 1.mp4 24.2 MB
  • 34. Random Forest Algorithm in Machine Learning A-Z/1. Random Forest Algorithm Theory.mp4 24.0 MB
  • 9. DataFrame Structures in Pandas Library/1. Creating Pandas DataFrame with List.mp4 23.7 MB
  • 5. Indexing, Slicing, and Assigning NumPy Arrays/2. Slicing One-Dimensional Numpy Arrays.mp4 23.4 MB
  • 10. Element Selection Operations in DataFrame Structures/5. Top Level Element Selection in Pandas DataFramesLesson 3.mp4 23.2 MB
  • 3. Creating NumPy Array in Python/9. Properties of NumPy Array.mp4 23.0 MB
  • 35. Support Vector Machine Algorithm in Machine Learning A-Z/1. Support Vector Machine Algorithm Theory.mp4 22.9 MB
  • 16. File Operations in Pandas Library/3. Data Entry with Excel Files.mp4 22.9 MB
  • 6. Operations in Numpy Library/1. Operations with Comparison Operators.mp4 22.2 MB
  • 4. Functions in the NumPy Library/5. Splitting One-Dimensional Numpy Arrays The Split.mp4 21.9 MB
  • 5. Indexing, Slicing, and Assigning NumPy Arrays/6. Fancy Indexing of One-Dimensional Arrrays.mp4 21.5 MB
  • 25. Evaluation Metrics in Machine Learning/1. Classification vs Regression in Machine Learning.mp4 20.9 MB
  • 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 20.7 MB
  • 16. File Operations in Pandas Library/5. Outputting as an Excel File.mp4 20.7 MB
  • 8. Series Structures in the Pandas Library/4. Object Types in Series.mp4 20.5 MB
  • 21. Matplotlib/1. What is Matplotlib.mp4 20.0 MB
  • 8. Series Structures in the Pandas Library/5. Examining the Primary Features of the Pandas Seri.mp4 19.9 MB
  • 8. Series Structures in the Pandas Library/2. Creating a Pandas Series with a Dictionary.mp4 19.2 MB
  • 5. Indexing, Slicing, and Assigning NumPy Arrays/4. Assigning Value to One-Dimensional Arrays.mp4 19.1 MB
  • 40. Recommender System Algorithm in Machine Learning A-Z/2. What is the Recommender System Part 2.mp4 18.8 MB
  • 30. K-fold Cross-Validation in Machine Learning A-Z/1. K-Fold Cross-Validation Theory.mp4 18.3 MB
  • 20. Object Oriented Programming (OOP)/1. Logic of Object Oriented Programming.mp4 18.2 MB
  • 37. K Means Clustering Algorithm in Machine Learning A-Z/1. K Means Clustering Algorithm Theory.mp4 18.0 MB
  • 4. Functions in the NumPy Library/7. Sorting Numpy Arrays Sort() Function.mp4 17.8 MB
  • 36. Unsupervised Learning with Machine Learning/1. Unsupervised Learning Overview.mp4 17.7 MB
  • 5. Indexing, Slicing, and Assigning NumPy Arrays/9. Combining Fancy Index with Normal Slicing.mp4 17.3 MB
  • 3. Creating NumPy Array in Python/3. Creating NumPy Array with Ones() Function.mp4 16.7 MB
  • 9. DataFrame Structures in Pandas Library/3. Creating Pandas DataFrame with Dictionary.mp4 16.6 MB
  • 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/3. Separating variables (Numeric or Categorical).mp4 16.6 MB
  • 11. Structural Operations on Pandas DataFrame/2. Removing Rows and Columns from Pandas Data frames.mp4 16.3 MB
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  • 33. Decision Tree Algorithm in Machine Learning A-Z/4. Decision Tree Algorithm with Python Part 3.mp4 15.4 MB
  • 24. First Contact with Machine Learning/2. Machine Learning Terminology.mp4 14.7 MB
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  • 3. Creating NumPy Array in Python/5. Creating NumPy Array with Arange() Function.mp4 12.7 MB
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  • 8. Series Structures in the Pandas Library/3. Creating Pandas Series with NumPy Array.mp4 12.6 MB
  • 53. Preparation for Modelling in Machine Learning/10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 12.0 MB
  • 3. Creating NumPy Array in Python/4. Creating NumPy Array with Full() Function.mp4 11.7 MB
  • 4. Functions in the NumPy Library/3. Detecting Least Element of Numpy Array Min(), Ar.mp4 10.7 MB
  • 49. First Organization/2. Loading the Statistics Dataset in Data Science.mp4 10.5 MB
  • 19. Fundamentals of Python 3/9. Exercise - Analyse in Python.mp4 8.9 MB
  • 39. Principal Component Analysis (PCA) in Machine Learning A-Z/3. Principal Component Analysis (PCA) with Python Part 2.mp4 8.8 MB
  • 3. Creating NumPy Array in Python/7. Creating NumPy Array with Linspace() Function.mp4 7.7 MB
  • 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/2. FAQ about Machine Learning, Data Science.html 15.7 kB
  • 41. First Contact with Kaggle/2. FAQ about Kaggle.html 11.2 kB
  • 18. Introduction to Data Visualization with Python/2. FAQ regarding Data Visualization, Python.html 8.8 kB
  • 24. First Contact with Machine Learning/5. FAQ regarding Machine Learning.html 6.8 kB
  • 24. First Contact with Machine Learning/4. FAQ regarding Python.html 6.4 kB
  • 1. Installations/4. 6 Article Advice And Links about Numpy, Numpy Pyhon.html 4.3 kB
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  • 24. First Contact with Machine Learning/3. Machine Learning Project Files.html 254 Bytes
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  • 12. Multi-Indexed DataFrame Structures/4. Quiz.html 205 Bytes
  • 13. Structural Concatenation Operations in Pandas DataFrame/7. Quiz.html 205 Bytes
  • 14. Functions That Can Be Applied on a DataFrame/10. Quiz.html 205 Bytes
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  • 19. Fundamentals of Python 3/11. Quiz.html 205 Bytes
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  • 20. Object Oriented Programming (OOP)/6. Quiz.html 205 Bytes
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  • 25. Evaluation Metrics in Machine Learning/5. Quiz.html 205 Bytes
  • 26. Supervised Learning with Machine Learning/2. Quiz.html 205 Bytes
  • 28. Bias Variance Trade-Off in Machine Learning/2. Quiz.html 205 Bytes
  • 29. Logistic Regression Algorithm in Machine Learning A-Z/7. Quiz.html 205 Bytes
  • 3. Creating NumPy Array in Python/10. Quiz.html 205 Bytes
  • 31. K Nearest Neighbors Algorithm in Machine Learning A-Z/5. Quiz.html 205 Bytes
  • 33. Decision Tree Algorithm in Machine Learning A-Z/7. Quiz.html 205 Bytes
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  • 37. K Means Clustering Algorithm in Machine Learning A-Z/6. Quiz.html 205 Bytes
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  • 41. First Contact with Kaggle/6. quiz.html 205 Bytes
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  • 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/7. Quiz.html 205 Bytes
  • 49. First Organization/4. Quiz.html 205 Bytes
  • 50. Preparation For Exploratory Data Analysis (EDA) in Data Science/5. Quiz.html 205 Bytes
  • 51. Exploratory Data Analysis (EDA) - Uni-variate Analysis/6. Quiz.html 205 Bytes
  • 52. Exploratory Data Analysis (EDA) - Bi-variate Analysis/15. Quiz.html 205 Bytes
  • 53. Preparation for Modelling in Machine Learning/12. Quiz.html 205 Bytes
  • 54. Modelling for Machine Learning/9. Quiz.html 205 Bytes
  • 55. Conclusion/2. Quiz.html 205 Bytes
  • 7. Pandas Library Introduction/3. Quiz.html 205 Bytes
  • 8. Series Structures in the Pandas Library/8. Quiz.html 205 Bytes
  • 9. DataFrame Structures in Pandas Library/5. Quiz.html 205 Bytes
  • 7. Pandas Library Introduction/2. Pandas Project Files Link.html 180 Bytes
  • 17. Code Files And Resources Python data analysis and visualization/1. Data Visualisation - Matplotlib Files.html 170 Bytes
  • 17. Code Files And Resources Python data analysis and visualization/2. Data Visualisation - Seaborn Files.html 170 Bytes
  • 17. Code Files And Resources Python data analysis and visualization/3. Data Visualisation - Geoplotlib.html 168 Bytes
  • 1. Installations/2. Notebook Project Files Link regarding NumPy Python Programming Language Library.html 155 Bytes
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
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  • 26. Supervised Learning with Machine Learning/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 48. Introduction to Machine Learning with Real Hearth Attack Prediction Project/4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html 108 Bytes
  • 41. First Contact with Kaggle/4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html 97 Bytes
  • 0. Websites you may like/[GigaCourse.Com].url 49 Bytes
  • 11. Structural Operations on Pandas DataFrame/0. Websites you may like/[GigaCourse.Com].url 49 Bytes
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